Stock Price Prediction

[alo7lika]

Is your feature request related to a problem? Please describe. Yes, the feature request is related to the problem of uncertainty in stock market investments. Investors often struggle to make predictions about future stock prices due to the volatile nature of the market. By developing a stock price prediction model, we can address this challenge by providing a data-driven approach to forecasting. This model can help investors understand potential future trends, identify buying or selling opportunities, and ultimately make more informed investment decisions.

Describe the solution you'd like The solution for the stock price prediction project involves a comprehensive approach that starts with data acquisition, where historical stock prices and relevant market indicators are gathered using APIs like Yahoo Finance or Alpha Vantage. Once the data is collected, it undergoes preprocessing to ensure cleanliness and consistency, including the removal of duplicates and handling missing values. Feature engineering is then performed to enhance the dataset with additional relevant features, such as moving averages and technical indicators, which can significantly improve model accuracy. Various regression algorithms, including Linear Regression, Decision Trees, and Random Forest Regression, are implemented and evaluated to determine the most effective model for predicting stock prices. The model's performance is assessed using metrics like Mean Absolute Error (MAE) and visualized through plots that compare predicted and actual prices. To make the solution accessible, a user-friendly web application is developed using Flask or Django, allowing users to input stock tickers and view predictions in real-time. Comprehensive documentation and well-commented code ensure that the project remains maintainable and encourages contributions from other developers. Overall, this solution not only addresses a significant challenge in the investment community but also showcases a blend of data science and machine learning techniques in a practical application.

Describe alternatives you've considered In exploring the stock price prediction project, several alternative approaches were considered alongside the primary regression algorithms. One alternative is to utilize time series analysis methods, such as ARIMA (AutoRegressive Integrated Moving Average) or Exponential Smoothing State Space Models (ETS), which are specifically designed for forecasting based on temporal data. These methods can capture trends and seasonality more effectively than traditional regression techniques. Another option was to leverage advanced machine learning techniques, such as Long Short-Term Memory (LSTM) networks, which are well-suited for sequential data and can potentially improve prediction accuracy by learning complex temporal patterns. Additionally, ensemble methods like Gradient Boosting and XGBoost were considered for their ability to combine multiple models to enhance prediction performance. While these alternatives offer promising avenues for improved accuracy, the chosen approach focuses on regression algorithms due to their simplicity, interpretability, and ease of implementation, making them more accessible for users with varying levels of expertise. This balance between performance and usability is crucial, especially for those who may be new to data science and machine learning.

Approach to be followed (optional) A clear and concise description of the approach to be followed.

Additional context Add any other context or screenshots about the feature request here.

add labels hacktoberfest and GSSOC ext24

assign me this project

[github-actions[bot]]

Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. Your contributions are highly appreciated! 😊

[SaiNivedh26]

@alo7lika It's Great to see you've developed different approach to this problem statements. Finally you can make a PR. Please make sure that you attach all necessary files and well documented README file consisting of final result of evaluation metrices of the Algorithms, which you've adopted